Guidance and navigation of autonomous vehicles, such as terrestrial rovers and unmanned aerial vehicles (UAVs), are often controlled with the aid of a global positioning system (GPS). In certain situations, however, GPS is unavailable. For example, if a rover is deployed on another planet, GPS cannot be used to control its navigation. Furthermore, if a UAV is flying in a location in which GPS signals may become blocked (e.g., deep within a canyon), GPS cannot be relied upon alone. Therefore, alternative techniques are needed to enable guidance and navigation of autonomous vehicles.
Various techniques have been developed to provide for guidance and navigation control, such as visual servo control, visual odometry, and structure from motion. Common to each of these methods is the problem of estimating the relative pose (rotation and translation) of an object between two images. For a single camera, the rotation and direction of translation are estimated, whereas in case of a stereo camera system, the rotation and the translation vectors are estimated.
Existing methods for pose estimation typically use point correspondence between the two views, which is provided by a feature-tracking algorithm, such as the Kanade-Lucas-Tomasi (KLT) algorithm. Given a minimal set of point correspondence, the relative pose can be estimated by a number of algorithms (the eight point algorithm, the five point algorithm, etc.). However, point correspondences returned by the feature tracker normally contain gross mismatches or large errors in feature point locations, which are commonly referred to as outliers. A central issue in accurate pose estimation is devising robust estimators that can reject such outliers. The most popular solution to this problem has been hypothesize-and-test methods, such as random sample consensus (RANSAC) and its variants. In these methods, hypotheses are generated by randomly choosing a minimal set of corresponding feature point pairs that are required to generate a hypothesis. A hypothesis is typically scored based on how many of the observations are well-explained by the hypothesis, and the hypothesis with the best score is declared the desired estimate. Most of the extensions to the basic RANSAC scheme focus on reducing the computation time, since generating a large number of hypotheses (which is required to obtain a good estimate with high probability) and scoring them is computationally expensive.
RANSAC and other hypothesize-and-test methods choose only one of the many hypotheses that are or can be generated. All other hypotheses are ignored, even those that may be quite close to the true pose. Each hypothesis can be thought of as a noisy “measurement” of the relative pose that is to be estimated. In principle, one should be able to average these measurements in an appropriate sense to compute a more accurate estimate than any of the individual measurements (i.e., hypotheses).
In view of the above, it can be appreciated that an alternative system and method for estimating pose would be desirable.